[1]} showed that, as \(n \rightarrow \infty \) , the maximum likelihood estimators, \(\hat{\theta }_{KO}\) , of \(\theta \)  under the [2]} model converge to \(\theta ^*_{KO}\) ; the values of \(\theta \)  that minimise the norm of \(\delta _{\theta }(\mathbf {x})\)  in the associated reproducing kernel Hilbert space norm with kernel \(\kappa \) . Under a Bayesian approach the influence of the prior distributions will diminish as \(n\)  grows meaning the posterior distribution will converge to a point mass at \(\theta ^*_{KO}\) . The convergence to quantities which depend on the choice of correlation function has been criticised by several authors [1]}, [4]}, [5]}, [6]}.
